Impact of luminous on Augmented Reality Response Time

Abstract

The goal of augmented reality is to combine elements of the actual environment with digitally created ones. In order to get realistic results, it is necessary to solve complex computer vision challenges. Some examples of these tasks include monitoring genuine 3D objects and assessing the lighting conditions of a scene. In this brief work, we explain how deep learning may be used to handle these two difficult problems in a way that is both accurate and reliable. As a potential solution to this issue, we have come up with the idea of feeding the network not only the currently active frame but also an estimate of the object's posture based on the preceding timestep in the sequence. Because of this, the network is able to repair any faults that occurred throughout the closed loop track  ing process. The creation of a synthetic frame of the tracked item allows for the acquisition of the feedback, which may be thought of as an estimate of the current object posture. As a result, our approach requires a 3D rendering of something along with instruction of the tracking device using this model. We think we're the first to use deep machine learning for 6-degrees-of-freedom (DOF) dynamic tracking of items, but we can't be sure. In both cases, the latest developments are achieved by training deep convolution neural network models on massive data sets.